Micro-ROS-based mobile robot embedded control system

Abstract

The invention discloses a micro-ROS-based mobile robot embedded control system, and relates to the technical field of robot control systems, and the control system comprises a real-time priority communication scheduling module; and the real-time priority communication scheduling module dynamically adjusts a scheduling polling period and a token generation rate based on a mode of combining a token bucket algorithm and a priority queue so as to solve the problem of delay jitter of multi-node concurrent communication. According to the invention, through real-time priority communication scheduling, bandwidth and priority are allocated according to message categories in a grading manner, low delay of a hard real-time control instruction is ensured, delay jitter caused by contention of high and low priority messages in a traditional CAN / UART bus is solved, and end-to-end low-delay, predictable and reliable control response is realized.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of robot control systems, and particularly relates to a micro-ROS mobile robot embedded control system. BACKGROUND

[0002] Traditional mobile robot control systems are mostly based on PC or high-performance embedded platforms, run ROS2 or self-developed middleware, and realize node-to-node communication through Ethernet or CANBus. However, as robots develop towards miniaturization, low cost and low power consumption, resource-constrained MCU (such as ARM Cortex-M) platforms are difficult to meet the real-time, reliability and endurance requirements at the same time: on the one hand, the centralized communication architecture in ROS2 is prone to critical message congestion on low-bandwidth buses; on the other hand, there is a lack of unified and dynamic solutions to node overload, fault switching and power management, which greatly reduces the performance of the system in complex field environments.

[0003] Patent CN111813138B discloses a real-time embedded control system for a legged mobile robot, which realizes centralized control of the robot and enhances the stability of the robot.

[0004] The above patent realizes real-time collection of joint and trunk information of the legged mobile robot, completes the calculation of the motion given of the robot according to the given remote controller, realizes centralized control of the robot, and enhances the stability of the robot, but when multiple nodes are concurrently communicated, time delay and jitter are generated, bandwidth and priority cannot be effectively allocated, and the problem of high delay of real-time control instructions is caused.

[0005] Therefore, the application provides a micro-ROS mobile robot embedded control system capable of hierarchically allocating bandwidth and priority according to message categories. SUMMARY

[0006] The application aims to provide a micro-ROS mobile robot embedded control system to solve the technical problem of being difficult to simultaneously meet the real-time, reliability and endurance requirements in the background art.

[0007] To achieve the above-mentioned purpose, the application provides the following technical scheme: a micro-ROS mobile robot embedded control system, the control system comprising:

[0008] a resource-constrained embedded real-time operating system master control unit integrated with a micro-ROS Master node;

[0009] a plurality of micro-ROS Client nodes, respectively deployed in a sensor acquisition unit, a motion driving unit and a power management unit;

[0010] The real-time priority communication scheduling module in each Client node is used for dynamically allocating bandwidth and priority according to the QoS requirement of the preset Topic, so as to ensure that the end-to-end delay is not more than 5 ms;

[0011] The Client node transmits messages through CAN Bus and UART bus, and is internally provided with CRC-16 data checking and retransmission mechanism;

[0012] The real-time priority communication scheduling module dynamically adjusts the scheduling polling period and the token generation rate in a manner of combining the token bucket algorithm and the priority queue, so as to solve the delay jitter problem in multi-node concurrent communication.

[0013] Preferably, the micro-ROS Client node further comprises an adaptive load balancing module, which is used for:

[0014] Real-time monitoring of node CPU usage, memory occupation and message publishing and subscribing frequency of each Topic;

[0015] Based on the online transfer learning algorithm Dyna-Q, the short-term load trend is predicted, and the calculation task and the communication task are dynamically redistributed among the Client nodes in a policy iteration manner;

[0016] The load transfer timing is coordinated through the inter-node heartbeat synchronization message, so as to ensure that the overall resource utilization rate is ≥80%, and the end-to-end delay fluctuation is not more than ±1 ms.

[0017] Preferably, the real-time priority communication scheduling module specifically comprises:

[0018] The scheduling submodule based on the EDF earliest deadline first algorithm is used for implementing pre-emptive scheduling on high-priority control topics;

[0019] The soft real-time data submodule based on the polling priority WRR is used for regularly sending low-priority data;

[0020] The dynamic QoS adjustment unit modifies the reliability parameter and the historical maximum sending interval of the Topic in real time according to the network congestion degree.

[0021] Preferably, in the adaptive load balancing module, the online learning algorithm further comprises:

[0022] The state indicator describes the node load in the form of a triple, which includes CPU utilization, memory occupation and message queue length;

[0023] The reward function is designed in the form of weighted sum of the system average response time and resource utilization rate, and gives the maximum positive reward when the response time is ≤5 ms and the utilization rate is ≥80%.

[0024] The action set includes operations such as "local execution", "migration to adjacent node", "load reduction into hibernation", etc., to ensure that the node load balancing and system real-time performance are considered.

[0025] Preferably, the micro-ROS Client node further comprises a fault detection and redundancy switching module for:

[0026] Sending a heartbeat packet every T=50ms;

[0027] If no response is received for N=3 consecutive heartbeat periods or CRC check fails, the node is determined to be faulty;

[0028] The standby node keeps mirroring the subscription state according to the topic, takes over the function of the faulty node, and triggers the Master node to update the Topic routing table.

[0029] Preferably, the fault detection and redundancy switching module further comprises:

[0030] A fault log ring buffer records the past M=100 fault events in timestamp format;

[0031] A JSON format reporting unit sends messages to the master unit through a secure MQTT channel to support remote online diagnosis.

[0032] Preferably, the micro-ROS Client node further comprises an energy-aware communication management module, which is used for:

[0033] Real-time collection of battery voltage, current and power consumption data, with an accuracy of 0.1% measurement value through an ADC interface;

[0034] Automatic adjustment of message sending frequency based on the remaining power threshold and the priority of each Topic;

[0035] When the remaining power is <20%, the system enters a low-power mode, only high-priority communication is retained, and other topics use a periodic wake-up strategy to ensure at least 2h continuous operation.

[0036] Preferably, the master unit uses an ARM Cortex-M7 architecture MCU with a clock frequency of 480MHz, built-in 2MB Flash, 512KB SRAM, and reserved external SPI Flash interface for storing micro-ROS image and log data.

[0037] Preferably, the control system further comprises a security encryption module, which is used for:

[0038] The Topic messages transmitted between the Client nodes are encrypted in AES-128 CTR mode;

[0039] The key negotiation between the Master and the Client is completed through the ECDH algorithm, and the session key is periodically hashed and refreshed based on SHA-256, so as to guarantee the communication security.

[0040] Preferably, the control system further comprises an OTA update module, and the OTA update module is used for:

[0041] pulling the differential firmware package from the remote server through HTTPS;

[0042] performing foreground downloading and background decompression when the task load is low;

[0043] after the updating is completed, automatically switching to the new firmware by restarting the micro-ROS Master node and checking the signature of the new image.

[0044] Compared with the prior art, the present application has the beneficial effects that:

[0045] 1. The present application realizes hierarchical allocation of bandwidth and priority according to message categories through real-time priority communication scheduling, guarantees low delay of hard real-time control instructions, solves the time delay jitter caused by the contention of high and low priority messages in the traditional CAN / UART bus, realizes low end-to-end delay, predictable and reliable control response;

[0046] 2. The present application realizes online learning load prediction and task migration based on Dyna-Q through online adaptive load balancing, monitors node resource usage in real time, dynamically redistributes computing and communication tasks among nodes, solves the performance bottleneck and real-time loss of control caused by node overload under the fixed scheduling strategy, improves the overall resource utilization of the system, reduces the delay fluctuation, and improves the operation stability;

[0047] 3. The present application realizes automatic detection of node disconnection and data verification failure through fault detection and redundancy switching, solves the control interruption and data loss caused by environmental interference or hardware failure in the field, improves the system robustness, guarantees the continuity of the control flow, and reduces downtime and manual intervention;

[0048] 4. The present application realizes automatic adjustment of the publishing frequency of different priority Topics and node sleep strategy according to the remaining power through energy consumption perception communication management, solves the problem that mobile robots are difficult to balance between low power consumption and real-time demand, guarantees key communication while improving the endurance time, and meets the long-time field operation demand. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 The figure is a schematic diagram of the control system framework of the present application. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the protection scope of the present application.

[0051] Please refer to Figure 1 An embodiment provided by the present application is a micro-ROS mobile robot embedded control system; the control system further comprises a security encryption module, and the security encryption module is configured to:

[0052] perform AES-128 CTR mode encryption on Topic messages transmitted between the Client nodes;

[0053] The Master and the Client complete key negotiation through an ECDH algorithm, and periodically refresh the session key based on SHA-256 to ensure communication security;

[0054] Further, system initialization and Master-Client configuration are performed:

[0055] Hardware power-on self-test: after the Master node MCU is powered on, Bootloader is first executed to perform self-test on the Flash, SRAM, peripheral interfaces (CAN, UART, ADC); the Client node synchronously performs self-test on local resources, and sends a heartbeat packet to the Master through the / hb_<node_id> topic, carrying node type and capability description (such as CPU core number, available memory);

[0056] Micro-ROS network topology construction: the Master node starts a micro-ROS Agent to listen to registration requests of all Client nodes, the Client nodes sequentially call rclc_executor_init() and rclc_node_init(), and send registration messages on the / node_register topic, and the Master registers the node ID, Topic relationship list and QoS configuration in the internal routing table after receiving the registration;

[0057] Key negotiation and secure channel establishment: the Master and each Client exchange public keys through the ECDH algorithm to generate a shared session key; the session key is initially hashed based on SHA-256 as an AES-128 CTR mode encryption key; all subsequent Topic messages are sent after being encrypted by AWS-128 CTR to ensure communication privacy and tamper resistance;

[0058] The system can form a predictable and safe ROS communication network after starting, and lays a foundation for real-time control.

[0059] Please refer to Figure 1 An embodiment provided by the application is: a micro-ROS mobile robot embedded control system; the control system comprises:

[0060] A master unit of a resource-limited embedded real-time operating system, integrated with a micro-ROS Master node;

[0061] A plurality of micro-ROS Client nodes, respectively deployed in a sensor acquisition unit, a motion driving unit and a power management unit;

[0062] A real-time priority communication scheduling module is arranged in each Client node, for dynamically allocating bandwidth and priority according to the QoS requirement of a preset Topic, to ensure that the end-to-end delay is less than 5ms;

[0063] The Client node transmits messages through a CAN Bus and a UART bus, and is internally provided with a CRC-16 data check and retransmission mechanism;

[0064] The real-time priority communication scheduling module dynamically adjusts the scheduling polling period and the token generation rate in a combination mode of a token bucket algorithm and a priority queue, to solve the delay jitter problem in multi-node concurrent communication;

[0065] The real-time priority communication scheduling module specifically comprises:

[0066] A scheduling submodule based on an EDF earliest deadline first algorithm, for implementing preemptive scheduling on a high-priority control topic;

[0067] A soft real-time data submodule based on a polling priority WRR, for regularly sending low-priority data;

[0068] A dynamic QoS adjustment unit, for real-time modification of the reliability parameter and the historical maximum sending interval of a Topic according to network congestion;

[0069] Topic classification and scheduling strategy:

[0070] High priority: / emergency_stop, / cmd_vel, soft and hard real-time requirement of end-to-end delay ≤5ms;

[0071] Medium priority: / odom, / sensor_data, delay ≤10ms;

[0072] Low priority: / status_report, latency ≤ 100ms

[0073] Token bucket + EDF earliest deadline first + WRR hybrid scheduling:

[0074] Each communication cycle T = 1ms, a fixed number of tokens are allocated to high priority Topic, if the token is insufficient, it will be preempted immediately; the EDF earliest deadline first submodule is run on the remaining bandwidth, and the data with high priority is scheduled in advance; the WRR submodule sends low priority messages in the idle window; the scheduling parameters (token generation rate, EDF earliest deadline first emergency threshold) are dynamically adjusted according to the error frame count on the bus;

[0075] Real-time process example:

[0076] t = 0ms: Master issues / cmd_vel command;

[0077] t = 0.1ms: Sensor node receives the command and publishes / cmd_vel_ack;

[0078] t = 0.5ms: Motion drive node converts control signal to PWM waveform and responds immediately;

[0079] The overall end-to-end latency is controlled within the range of 2-4ms;

[0080] Realize end-to-end predictable low latency, avoid delay or loss of critical control commands.

[0081] Please refer to Figure 1 An embodiment provided by the application: a micro-ROS mobile robot embedded control system; the micro-ROS Client node further comprises an adaptive load balancing module, and the adaptive load balancing module is used for:

[0082] Real-time monitoring of node CPU usage, memory occupation and message publishing and subscribing frequency of each Topic;

[0083] Based on the online transfer learning algorithm Dyna-Q, the short-term load trend is predicted, and the calculation task and the communication task are dynamically redistributed among the Client nodes in a policy iteration manner;

[0084] The load transfer time is coordinated through the inter-node heartbeat synchronization message, so that the overall resource utilization is greater than or equal to 80%, and the end-to-end delay fluctuation is not more than ±1ms;

[0085] In the adaptive load balancing module, the online learning algorithm further comprises:

[0086] State indicator, describes node load in triplets, including CPU utilization, memory occupation and message queue length;

[0087] Reward function, designed as weighted sum of system average response time and resource utilization, gives maximum positive reward when response time ≤ 5ms and utilization ≥ 80%;

[0088] Action set includes operations such as "local execution", "migration to adjacent node", "load shedding into hibernation", etc., to ensure node load balancing and system real-time performance;

[0089] Adaptive load balancing:

[0090] State monitoring and learning model: collect CPU utilization, memory occupation and message queue length every 10ms; state triplets are sent to Dyna-Q algorithm, and Q table is updated online combined with historical data;

[0091] Task migration and redistribution: if CPU > 90% and average latency > 6ms, trigger "migration to adjacent node" action; through / node_load heartbeat topic, notify target node to receive tasks and dynamically update micro-ROSService call routing;

[0092] Real-time process example: sensor node detects image preprocessing task causing CPU utilization 95%, online learning model recommends migrating part of preprocessing algorithm to driver node, driver node starts corresponding Service and begins to receive and process image frames, system overall utilization returns to ≈ 85%;

[0093] In high load scenarios, ensure real-time performance of critical tasks and balanced utilization of system resources.

[0094] Please refer to Figure 1 , the present application provides an embodiment: a micro-ROS mobile robot embedded control system; the micro-ROS Client node further comprises a fault detection and redundancy switching module, which is used for:

[0095] Send heartbeat packet with period T = 50ms;

[0096] If no response is received for consecutive N = 3 heartbeat periods or CRC check fails, the node is determined to be faulty;

[0097] The standby node keeps mirror subscription state according to the topic, takes over the function of the faulty node, and triggers the Master node to update the Topic routing table;

[0098] The fault detection and redundancy switching module further comprises:

[0099] Fault log ring buffer, record past M=100 fault events in timestamp format;

[0100] JSON format-based reporting unit, send messages to the master unit through a secure MQTT channel to support remote online diagnosis;

[0101] Fault detection and redundancy switching:

[0102] Periodic heartbeat detection: every 50ms heartbeat, 3 consecutive non-responses or CRC check failures determine a fault;

[0103] Redundant node takeover: standby node long-term monitoring / hb_backup topic, maintaining functional image; immediately subscribe to all topics of the fault node after the fault occurs, and replace the node ID in the Master routing table; at the same time, write the fault event (timestamp, node ID, fault type) into the ring buffer, retaining the last 100;

[0104] Real-time process example: motion drive node fails due to external impact, heartbeat loss; standby drive node completes switching within 150ms, control flow continues seamlessly, motion command is not lost; system logs record switching history for post-diagnosis;

[0105] Significantly improve the robustness and reliability of robots in complex environments.

[0106] Please refer to Figure 1 An embodiment provided by the present application: a micro-ROS mobile robot embedded control system; the micro-ROS Client node further includes an energy consumption aware communication management module, which is used for:

[0107] Real-time acquisition of battery voltage, current and power consumption data, 0.1% precision measurement value through ADC interface;

[0108] Based on the remaining power threshold and the priority of each Topic, automatically adjust the message sending frequency;

[0109] When the remaining power is <20%, the system enters low-power mode, only high-priority communication is retained, and other topics use a periodic wake-up strategy to ensure at least 2h continuous operation;

[0110] Further, power collection and strategy threshold: ADC real-time acquisition of battery voltage and current, precision 0.1%; set power thresholds: 80%, 50%, 20%;

[0111] Dynamic communication adjustment: power ≥ 50%, maintain all topics at normal frequency; 20% ≤ power < 50%, reduce low-priority topic frequency to 50%; power < 20%, only keep high-priority topics, low-priority topics use 1s wake-up strategy;

[0112] Real-time process example: power decreases from 55% to 45%, system automatically adjusts / status_report frequency; endurance increases from original 1.5h to 2.1h;

[0113] Under the premise of ensuring key control, prolong the endurance time of the robot, balance real-time and endurance needs.

[0114] The implementation is supplemented as follows:

[0115] Master control unit: processor: ARM Cortex-M7, 480MHz, 2MB Flash, 512KB SRAM; peripherals: CAN transceiver (ISO 11898), UART adapter module, SPI Flash (for storing logs and firmware), ADC (collecting battery voltage and current);

[0116] Client node: sensor acquisition unit: additional IMU, laser radar and other peripherals, connected to MCU through SPI / I2C; motion driving unit: PWM drives motor controller, outputs PWM through GPIO and TIM timer; power management unit: power detection circuit (voltage division + current sampling resistor), ADC interface acquisition;

[0117] Communication bus: ACN Bus: 500kbps, using CRC-15 check; UART: 1Mbps, including CRC-16 data check and retransmission mechanism.

[0118] System integration and testing:

[0119] Simulation verification: load robot model in Gazebo, use micro-ROS plug-in to simulate network delay and packet loss; adjust scheduling parameters so that high-priority instructions can still arrive in ≤5ms in the simulation environment.

[0120] Hardware testing: monitor CAN message timing through logic analyzer; perform path tracking and emergency stop testing in actual field to test fault switching and endurance management effect.

[0121] Performance indicators: control delay: high-priority instruction end-to-end ≤5ms; resource utilization: system load remains between 80%-90%; fault switching time: ≤150ms; endurance improvement; normal mode 1.5h - low-power mode ≥2h.

[0122] Optional implementation and equivalent variations:

[0123] In the scheduling algorithm, the token bucket can be replaced by the leaky bucket;

[0124] The load balancing learning algorithm can adopt SARSA and deep Q-learning.

[0125] The fault switching heartbeat period and threshold can be adjusted according to the application scenario.

[0126] The communication bus can also be extended to CAN FD or Ethernet TSN.

[0127] Working principle: The system is composed of a master control unit integrated with a micro-ROS master node and a plurality of client nodes running on a resource-limited embedded RTOS. The master node is responsible for network topology management, topic routing and security key negotiation. The client node carries sensor acquisition, motion driving, power management and other functional modules, and communicates with the master and other client nodes through the micro-ROS middleware on the CAN Bus / UART.

[0128] The client node is built-in real-time priority communication scheduling engine, adopts token bucket + EDF earliest deadline first + WRR hybrid strategy, classifies and schedules command control, high-speed feedback and state reporting according to QoS level, ensures that the end-to-end delay of high-priority message is controllable within 5ms, and adjusts the bus congestion through dynamic QoS parameters.

[0129] During system operation, the client node reports load, heartbeat and power information in real time, the master node makes load balancing decisions based on online learning algorithm, and triggers redundancy switching or task migration when the node fails or is overloaded. At the same time, the energy-aware module dynamically adjusts the communication frequency and sleep strategy according to the remaining power to prolong the endurance and consider the real-time performance.

[0130] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be regarded as exemplary and non-limiting, and the scope of the present application is defined by the appended claims rather than the above description, and therefore all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. A micro-ROS-based mobile robot embedded control system, characterized in that: The control system comprises: a master unit of a resource-limited embedded real-time operating system, integrated with a micro-ROS Master node; a plurality of micro-ROS Client nodes respectively arranged in a sensor acquisition unit, a motion driving unit and a power management unit; a real-time priority communication scheduling module arranged in each of the Client nodes, configured to dynamically allocate bandwidth and priority according to preset QoS requirements of a Topic, and ensure that an end-to-end delay is less than 5 ms; the Client nodes are configured to transmit messages through CAN Bus and UART bus, and are internally provided with a CRC-16 data check and retransmission mechanism; the real-time priority communication scheduling module is configured to dynamically adjust a scheduling polling period and a token generation rate in a manner that the token bucket algorithm is combined with a priority queue, so as to solve a delay jitter problem in concurrent communication of multiple nodes.

2. The micro-ROS-based embedded control system for mobile robots according to claim 1, wherein: The micro-ROS Client node further comprises an adaptive load balancing module, which is configured to: monitor, in real time, a CPU usage rate, a memory occupation and a message publishing and subscribing frequency of each Topic; predict a short-term load trend based on an online migration learning algorithm Dyna-Q, and dynamically re-distribute a calculation task and a communication task among the Client nodes in a policy iteration manner; coordinate a load transfer timing through an inter-node heartbeat synchronization message, so as to ensure that an overall resource utilization rate is greater than or equal to 80%, and an end-to-end delay fluctuation is less than or equal to ±1 ms.

3. The micro-ROS-based embedded control system for mobile robots according to claim 1, wherein: The real-time priority communication scheduling module specifically comprises: a scheduling submodule based on an EDF earliest deadline first algorithm, configured to implement pre-emptive scheduling on a high-priority control theme; a soft real-time data submodule based on a polling priority WRR, configured to periodically send low-priority data; a dynamic QoS adjustment unit configured to modify, in real time, a reliability parameter and a historical maximum sending interval of the Topic according to a network congestion degree.

4. The micro-ROS-based embedded control system for mobile robots according to claim 2, wherein: In the adaptive load balancing module, the online learning algorithm further comprises: a state representor configured to describe a node load in a triple form, the triple comprising a CPU utilization rate, a memory occupation and a message queue length; a reward function configured to be designed in a weighted sum form of a system average response time and a resource utilization rate, and to give a maximum positive reward when the response time is less than or equal to 5 ms and the utilization rate is greater than or equal to 80%; an action set comprising operations such as "local execution", "migration to an adjacent node" and "load reduction into sleep", so as to ensure that a node load balancing and system real-time performance are considered.

5. The micro-ROS-based embedded control system for mobile robots according to claim 1, wherein: The micro-ROS Client node further comprises a fault detection and redundancy switching module, which is configured to: send a heartbeat packet at a cycle T=50 ms; if no response is received for N=3 consecutive heartbeat periods or CRC check fails, it is determined that a node is faulty; a standby node takes over a function of the faulty node according to a mirror subscription state of a theme, and triggers a Master node to update a Topic routing table.

6. The micro-ROS-based embedded control system for mobile robots according to claim 5, wherein: The fault detection and redundancy switching module further comprises: a fault log ring buffer configured to record, in a time stamp format, M=100 past fault events; The reporting unit based on the JSON format sends messages to the master unit through a secure MQTT channel to support remote online diagnosis.

7. The micro-ROS-based embedded control system for mobile robots according to claim 1, wherein: The micro-ROS Client node also includes an energy-aware communication management module, which is used for: Real-time acquisition of battery voltage, current and power consumption data, with an accuracy of 0.1% measurement value through an ADC interface; Based on the remaining power threshold and the priority of each Topic, the message sending frequency is automatically adjusted; When the remaining power is <20%, the system enters a low-power mode, only high-priority communication is retained, and other topics use a periodic wake-up strategy to ensure at least 2h continuous operation.

8. The micro-ROS-based embedded control system for mobile robots according to claim 1, wherein: The master unit uses an ARM Cortex-M7 architecture MCU with a main frequency of 480MHz, built-in 2MB Flash, 512KB SRAM, and reserved external SPI Flash interface for storing micro-ROS image and log data.

9. The micro-ROS-based embedded control system for mobile robots according to claim 1, wherein: The control system also includes a security encryption module, which is used for: AES-128 CTR mode encryption of Topic messages transmitted between Client nodes; Key negotiation between Master and Client is completed through the ECDH algorithm, and periodic hash refresh of the session key is based on SHA-256 to ensure communication security.

10. The micro-ROS-based embedded control system for mobile robots according to claim 1, wherein: The control system also includes an OTA update module, which is used for: Pulling a differential firmware package from a remote server through HTTPS; Performing foreground download and background decompression under low task load; After updating, automatically switch to the new firmware after restarting the micro-ROS Master node and verifying the signature of the new image.

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